In Tab. REF , we provide an ablation study of different components in REDO using SAIL-VOS 3D data.
1) We first replace the temporal aggregator \(f_\text{agg}\)  with an average pooling layer where features of different frames are averaged and fed into the shape reconstruction and velocity field network. The results are shown in the \(1^\text{st}\)  row of Tab. REF  (avg. pooling). The performance drops by -3.6 IoU, +0.065 mCham., and +0.13 mACD.
2) We then study pixel-aligned feature representations \(_{\mathbf {p}}, _{\mathbf {p}}\) . We replace these two features with the feature map of the entire input frame following OFlow [1]} but still keep the transformer to aggregate these feature maps. Results of this ablation are reported in Tab. REF  (w/o alignment). Compared to REDO, this setting greatly hurts the results (-7.8 mIoU, +0.290 mCham., -0.38 mACD) as the network can no longer handle partial observations and the 3D predictions don't well align with the visual input.
3) In many real-world tasks, ground-truth meshes of different time-steps are not corresponded. Conceptually, REDO could adapt to this setting. This is because all components are differentiable and the flow-field network could be used as a latent module for shape reconstruction. To mimic this setting, we train REDO using only the shape reconstruction loss \(_\text{shape}\) . As shown in Tab. REF  (w/o \(_\text{temp}\) ), the model still recovers objects at the canonical frame. However, mACD increases significantly (+1.65).
